As pointed out by [1]}, ML for medical imaging is susceptible to different domain shifts that affect algorithmic performance and robustness in new environments. Population shifts occur when the train and test populations exhibit different characteristics that might include the prevalence of diseases, for example prevalence of lung nodules in polluted urban environments is higher than in a rural setting. An ML model trained in one of the two settings is not able to condition on the true causal links that are unseen in the images, hence, treats both populations the same way.
